In this chapter, we will discuss our findings from the results. We will in this chapter compare the two situations to our research question and previously discussed literature. The discussion can be seen as a cross-case analysis. We will not differentiate on the current situation and the future situation in separate sections as in chapter four. For the discussion, they are intertwined.
5.1 HSCM
Transportation has been pointed out to be an essential operation in HSCM (Syed et al., 2013). Syed et al. (2013) claimed that the transportation of material and products affect the operationality of the hospital direct and indirect. They argued that transportation in HSCM is often complicated, which leads to delays and expensive care. We found that the transportation of biological materials and RI is critical to OUH as it is a part of the diagnostic and treatment of patients. Landry and Beaulieu (2013) claimed in their study that small entities in hospitals are dependent on rapid deliveries of medical supplies. We argue that this can be achieved both in the current and future situation. Currently, to achieve a flow of biological material between hospitals and clinics 24/7, OUH needs to hire taxis outward the period from 08:00 to 15:30, where OUH do not operate themselves.
However, we argue that in a future situation, this can be done more efficiently and reduce costs as our findings show that digitalised UAVs will be able to transport 24/7. Haidari et al. (2016) claimed that UAVs ability to transport 24/7 can cut costs in SCs. We support this as OUH will be able to cut 2.600.000 NOK by removing the taxi service entirely, according to our research.
Furthermore, Bechtsis et al. (2017) have suggested that the implementation of digitalisation in HSCM can reduce costs without degrading the patient service.
The findings from our results support this statement. When comparing the current and future situation, we see that OUH can reduce their costs per km related to transportation by using UAVs. Based on the findings from Choi-Fitzpatrick et al.
(2016) and Scott and Scott (2017), we have estimated that the operational cost of UAVs is 0.26 NOK per km. The operational cost of the current vehicles at OUH is 1.76 NOK per km. Consequently, OUH can reduce their transportation costs by 85 % per km with UAV transportation. Kowalski (2009) highlighted the
importance of not reducing costs in a way that degrades the delivered patient service, which D. Q. Chen et al. (2013) agree on. When comparing the two situations, we argue that a UAV based situation will reduce the operational costs in a way that does not degrade the patient service.
We found that an important factor in the patient service is OUH’s ability to safeguard the patient needs. Ageron et al. (2018) stated that waiting times are one of several complex issues regarding the patient needs at any hospital. Our study has shown that there are long waiting times for responses towards analysing of samples at OUH. We see a difference in the delivery times when comparing the two situations. In the current situation, we found that the transportation of samples between Rikshospitalet and Ullevål can take up to five hours. In a future situation, we saw that the same transportation will take 10 minutes. Yoo et al. (2018) have stated that UAVs have the ability of fast transportations. We support this as UAVs can fly in a direct route. We argue that the peaks, queues, delays, and waiting times for patients at OUH can be partitioned if the UAVs can transport based on a schedule. We found that UAVs will not remove the associated ‘peaks’, but that they will contribute to smoothen out the curve and more evenly distribute the demand.
Our case showed that the current structure at OUH is characterised by daily
‘peaks’ related to the sampling of biological material. Between 06:00 and 15:00 OUH, our research has shown a demand for 41.000 samples, where the majority are classified as Elektiv-samples and others as ØH-samples. Due to a more reliable and fast delivery system, we found that all samples can be treated within an hour and with equal urgency. We argue therefore that there will be no
difference between Elektiv- and ØH samples, which we found to be a more efficient structure. However, our study shows that a future structure will require an investment of approximately 7.812.000 MNOK. Although the infrastructural cost is a substantial amount, the results have shown the potential to decrease the operational costs. Nonetheless, to reduce the operational costs, there must be an infrastructure present, and we therefore claim the investment to be necessary.
We have seen in the literature that the hospital structure remains fragmented and manual. G. Johnson (2014) highlighted that as a result, processes have become
slow and that the HSCs are unstructured. Our research has shown that the current structure of OUH has only evolved because a need has occurred and has been made without any real plan. Consequently, this supports the findings
from Duffield et al. (2007), who claim that hospitals have struggled to find a strategically correct structure. A proposed countermeasure to improve the
organisation structure and speed up processes is to increasingly use innovation (S.
M. Lee et al., 2011). However, literature has pointed out that in hospitals it is a hard task to benefit from innovations due to a rapid innovation pace and being that hospitals are knowledge-intensive and struggle to keep track with recent
developments (D. Q. Chen et al., 2013). The fact that OUH is planning to use UAVs as their primary way of transporting biological material emphasises the statements by De Vries and Huijsman (2011) that a digital structural change is happening in HSCM. In the same way, UAVs can be considered as innovation and knowledge demanding which D. Q. Chen et al. (2013) state makes it increasingly difficult for HSCM to implement.
Chong et al. (2015) have suggested that digitalisation can reduce complexity in HSCM. Our study shows that a future situation with digitalised UAVs will remove the need of having several transportation methods which we find is a source to reduced complexity. OUH can focus on one system that is connected to a control centre, but we found that there will have to be an undefined number of UAVs that needs to be monitored. However, the findings showed that the UAVs will fly based on a schedule, which we argue enhance the flow of transporting biological materials between departments. In line with Seuring (2013) we see that an investment in UAVs can be a strategic investment to make HSCM processes more efficient. But, our findings show that a future situation needs to have a back-up system if there are any malfunctions related to UAVs.
Brambilla and Capolongo (2019) argue that multiple operations in HSC contribute to pollution and are not environmental friendly. Furthermore, Buffalo et al. (2014) claim that hospitals have not been focusing on environmental solutions but solely prioritised the care delivery model (LaPointe, 2016). This is in accordance with our findings. From the results of the current situation, we see that OUH has given little thoughts on how their SC affects the environment. Nevertheless, the
literature recognises a change in the hospital sector (Brambilla & Capolongo,
2019), as their operations contribute to a significant part of emissions (Savage &
Vernon-Mazetti, 2017). Consequently, HSCM wants to become more
environmentally friendly. When looking at the future situation, we found that OUH acknowledges that their operations can be more environmentally-friendly and look at the UAV-implementation as a green investment. However, the research showed that the investment in UAVs is not to make OUH’s SC greener, but it will be regarded as an additional benefit.
5.2 Digitalisation
Empirical findings have claimed that digitalisation is a trend that can provide several sustainable advantages to SCM, such as cost efficiency, eco-friendly solutions, and safety (Cavada et al., 2014). However, Gibb and Haar (2009), McKone‐Sweet et al. (2005) and Chandra and Kachhal (2004) have all found that HSCM has not been able to benefit from the trends of SCM. Our research
supports the literature as we found OUH’s current transportation methods to be manual and do not use any kind of digitalisation. Both Cavada et al. (2014) and Gomez et al. (2015) has stated that digitalised UAVs are cost-efficient and provide fast and safe deliveries. Our research has shown that to deliver safe and efficient treatment in a cost-efficient way is important to OUH. We found that currently, OUH has not adopted a digitalised transportation to their HSC and can not benefit from the trends presented by Cavada et al. (2014). However, our case shows that a future situation, with digitalised UAVs, can draw on the benefits of a digital SC and transport biological materials and RI more time efficient than current transportation methods.
Gharibi et al. (2016) have stated that digitalised UAVs use smart technology in their operations. We found that the UAVs used for transportation in OUH’s SC will be digitalised and use smart technology to “communicate”, track samples, and share their positions. From the results of the current situation, we identify an issue concerning the tracking of vehicles and samples at OUH. de la Torre-Bueno (2014) has stated that the lack of tracking systems in HSCM degrades the patient service and patient safety due to mix-ups of the samples. A report from Krogstad et al. (2014) showed that 59 % of all samples are misidentified before being analysed. We found that in a future situation, the misidentified samples are
predicted to decrease by 45 % due to traceability. However, we can not conclude that this is isolated coherent with transportation. We acknowledge that there are several internal processes between sampling and analysis, and we can not determine which SC process leads to misidentification. We argue that with increased traceability on all processes, this can be reduced. However, from our research, an estimated 45 % reduction has been connected to a future digitalised transportation with the ability to track samples. In accordance with Cavada et al.
(2014), we argue that this is related to the integration of physical-, digital- and human systems. Moreover, tracking of samples increases the safety and ensures that samples are ethically handled, which was highlighted by Meslin and Quaid (2004) as an important part of accommodating to the patients’ needs. Although there are positive aspects of tracking, we argue that a tracking system could be adopted in OUH’s SC without having the need of implementing digitalised UAVs.
On the other hand, the previous theory has been split in whether UAVs themselves are a safe transportation method. Several scholars have addressed digitalised UAVs as a safe transportation method as it is autonomous and reduces traffic accidents (Afman et al., 2018; Haidari et al., 2016; Kaya et al., 2016).
Zhang et al. (2013), on the other hand, claim that there is a risk for the UAVs to fall down or collide into something. Based on our research, we find both
arguments to be valid. The UAVs will be autonomous due to digitalisation and use sensors to navigate in established corridors that reduce the risk of colliding into objects or people, which Regjeringen (2018) has stated to be important to make the transportation safe. Afman et al. (2018) found that UAVs reduce
potential casualties related to transportation. As there will be no people physically transporting the samples, as the UAVs are autonomous and unmanned, we
therefore find a total reduction of potential casualties for transporters. However, we support Afman et al. (2018), and Zhang et al. (2013) who claim that UAVs may fall down and risk casualties for people on the ground as there may be malfunctions in the digitalised systems. Nevertheless, we argue that the current manual transportation operations are less safe than a future situation due to that they are manned and pose potential injury threats to transporters. However, a future situation poses more threats to the surroundings on the ground.
Furthermore, there was acknowledged a risk that the digital systems of UAVs being hacked or hijacked. Regjeringen (2018) has addressed this as a safety issue.
However, in line with Afman et al. (2018), our findings showed that the UAVs will be regulated and there will be legislation concerning security measures, like sensors. To reduce the risk of UAVs to harm someone or something if they fall or collide, “Luftfartstilsynet” has stated that there will be established corridors where the UAVs can operate. Based on sensors, the UAVs will fly safely within these corridors. Nevertheless, we find the current transportation methods to be safer than UAVs, due to an uncertainty regarding the relation between digitalisation and potential hacking and hijacking attempts.
This research has highlighted the eco-friendly perspective in OUH’s SC. From the current situation, we saw that OUH uses ground-based transportation methods driven on gasoline, which scholars claim is a significant contributor to a severe amount of environmental threats (Azadi et al., 2015; Björklund, 2011). Barth et al.
(2015) find that ground-based and gasoline driven vehicles contribute to CO2 emissions and particulate matter pollution. It has been suggested that digitalisation can make transportation more eco-friendly (Brambilla & Capolongo, 2019). In a future situation, we found the transportation method to be electrically driven and aerial-based. In line with Rosser Jr et al. (2018), we find the electrical driven UAVs to be more eco-friendly than ground-based vehicles. On the other hand, we found that the electrical engine in UAVs limits the carrying capacity. This has been pointed out by Figliozzi (2018), which compared UAVs to trucks.
Figliozzi (2018) gets support from Haidari et al. (2016) who have highlighted the carrying capacity of UAVs to be an issue in SCM. By comparing the two
situations, we did find that the current vehicles have a higher capacity than the UAVs. Nevertheless, our research has shown that this is not a potential issue to OUH. Lohn (2017) found that UAVs are more efficient for ‘last-mile’-deliveries, which are characterised by low weight parcels. The deliveries of biological samples and RIs are relatively light and weigh up to four kgs and will be
transported on short distances. However, to cope with the amount of samples, we find that there will have to be frequent departures and multiple UAVs operating at once. We acknowledge that the capacity restraint is dependent on the case and what is being transported.
A surprising finding was that digitalised UAV based transportation creates predictability in deliveries. To our knowledge, there is no previous literature addressing digitalisation as a source to predictability in HSCM. Bechtsis et al.
(2017) and Barth et al. (2015) have highlighted the UAVs ability to deliver fast and reduce costs. However, we find that the UAVs does not only affect the operational costs but creates repercussions in the SCM. We argue that predictable deliveries are affecting the whole HSC as the hospital will be able to plan
operations more accurately. We argue that this is coherent with information sharing and the ability of UAVs to communicate where they are and when they will arrive (Michel, 2017). We agree with Michel (2017), and find that
information sharing around a ‘digital core’ can positively affect the departments planning- and forecasting activities. Our study has shown that predictable transportation times enables the possibility of producing the exact amount of RI needed. We found that this may reduce the production cost by 50%.
Consequently, in relation to more predictable deliveries, and information retrieved from OUH documents, there can be additional reduction of 30 % in salary costs (Appendix 10.3). We argue that both cost reductions are a result of digitalised UAVs that address issues that the current, un-digitalised transportation methods struggle with, such as unpredictability and uncertainty in transportation.
5.3 Sustainability
Savage and Vernon-Mazetti (2017) claim that multiple operations in HSCM are a source to the pollution of the environment. Barth et al. (2015)’s study found that transportation is a main contributor to the increased pollution. Our results support this finding, where currently OUH’s vehicles drive 484.279 km yearly and emit approximately 72,6 tonne of CO2-emissions each year. Compared to a future situation, UAVs will contribute to zero CO2-emissions due to an electrical power source which Barth et al. (2015) have stated. Consequently, we measure the UAVs to be more sustainable in terms CO2-emissions. This has a positive effect on the planet pillar (Elkington, 1997). By using UAVs instead of ground-based vehicles, we find that there will be a reduction of road wear, which is already heavily impacted. Also, UAVs will not contribute to any particulate matter pollution. We therefore argue that a future situation with UAV based
transportation will remove many of the negatives that are associated with the
current situation and its relation to pollution, which contributes to a more sustainable environment (Elkington, 1997).
Literature has identified noise as harmful to the environment (Goines & Hagler, 2007). Comparing the two situations, we saw that both types of vehicles are a source to environmental noise. Paviotti and Vogiatzis (2012) found that an average car emits 75 dB of noise in the city centre. According to Lohn (2017), UAVs emit between 30 and 80 dB of noise. The noise from UAVs will be different from cars and depends on the altitude that the UAVs will operate in. However, we argue that the UAVs will not create a noticeable louder noise than the vehicles used in the current situation, but we acknowledge that the noise will be of a different kind. Therefore, we find the noise to be disturbing to the surroundings, which corresponds with Sinibaldi and Marino (2013)’s findings.
Our research showed that an implementation of digitalised UAVs would change the need for human capital at OUH. Literature has pointed out that human capital has been necessary in businesses for decades and is essential for the overall performance and competitiveness (Barney, 1991; Hitt et al., 2017). In the current situation, we found that three transporters are working at OUH’s transport station.
Autonomous UAVs will replace these in a future situation. However, in a future situation, we saw a need for a control centre, monitoring the UAV transportation.
We assume that to keep the infrastructure operational 24/7, there will be a need for at least three operators that can work eight hours per day each. Consequently, we argue that replacing the transporters with UAVs is not in line with the people pillar (Elkington, 1997), but we see that the UAVs creates new positions. This corresponds with the findings of Hanifan and Timmermans (2018) that
digitalisation is a source to new positions that interacts and digitalisation (Merlino
& Sproģe, 2017).
Based on our findings, UAVs will reduce delays in the HSC that currently cause employees to work overtime. We argue that overtime can be reduced due to a more predictable and optimal flow of processes in OUH’s SC, where employees can, to a higher degree, be sure of the process- and transportation times due to information sharing. Furthermore, this can be the foundation for less stressful working environments. We argue that a more predictable schedule will let
employees plan upcoming events and reduce necessary overtime that is related to delays.
Staniškienė and Stankevičiūtė (2018) claim that the people term is often overlooked in SCM. However, our study shows the opposite. One of the main reasons for OUH to implement UAVs was to enhance patient service. We found that UAVs will be able to contribute to more efficient and predictable responses,
Staniškienė and Stankevičiūtė (2018) claim that the people term is often overlooked in SCM. However, our study shows the opposite. One of the main reasons for OUH to implement UAVs was to enhance patient service. We found that UAVs will be able to contribute to more efficient and predictable responses,